**Prerequisite:** Exposure to relevant concepts at undergraduate level and instructor consent**Contents**

The course provides a strong foundation in theory and methods of modeling randomness and data analysis in engineering applications. Specific topics include, review of calculus-based probability concepts, common distributions, expectation, moment generating functions; sampling statistics, order statistics, properties of sample mean, Central Limit Theorem. Sampling from a Normal distribution; Parameter estimation, maximum likelihood estimators, interval estimates; bias, efficiency and consistency of point estimators; sampling plans, sequential tests, Hypothesis testing, common tests concerning means, variances, goodness-of-fit, likelihood ratio test, Neyman-Pearson lemma; Regression models, design of experiments.

**References**

- Douglas C. Montgomery, Larry Faris Thomas and George C. Runger (2003) Engineering Statistics, 3rd edition, John Wiley & Sons.
- Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer (2007) Mathematical Statistics with Applications, 7th edition, Duxbury Resource Center.
- John A. Rice (1994) Mathematical Statistics and Data Analysis, 3rd edition, Thomson Learning
- George Casella and Roger Berger (2004) Statistical Inference, 2nd edition, Thomson Learning.
- Ajit C. Tamhane and Dorothy D. Dunlop (1999) Statistics and Data Analysis: From Elementary to Intermediate, Prentice Hall